Abstract: A method includes receiving map data with a plurality of map lane edges and a plurality of perception lane edges of a roadway segment. The method includes determining a probability score of alignments that each indicate whether at least one of the map lane edges of the alignment is to be a same lane line as one of the perception lane edges of the alignment. The method includes resolving an ambiguity when no probability score of the roadway segment satisfies at least one alignment criterion, and determining a resolved alignment between the perception and map lane edges. This includes applying multiple ambiguity stages each with a different ambiguity test and that determine whether to increment a non-ambiguity count depending on the magnitude of likelihoods related to the alignments. The resolved alignment exists when the alignment between lane edges has a highest non-ambiguity count.

Abstract: A method includes receiving map data with a plurality of map lane edges and a plurality of perception lane edges of a roadway segment. The method includes determining a probability score of alignments that each indicate whether at least one of the map lane edges of the alignment is to be a same lane line as one of the perception lane edges of the alignment. The method includes resolving an ambiguity when no probability score of the roadway segment satisfies at least one alignment criterion, and determining a resolved alignment between the perception and map lane edges. This includes applying multiple ambiguity stages each with a different ambiguity test and that determine whether to increment a non-ambiguity count depending on the magnitude of likelihoods related to the alignments. The resolved alignment exists when the alignment between lane edges has a highest non-ambiguity count.

Abstract: A perception and map edge association system for an autonomous vehicle includes one or more controllers executing instructions to receive a plurality of perception lane edge candidates that are each representative of a perception lane edge and a plurality of map lane edge candidates that are each representative of a map lane edge. The one or more controllers identify a discrepancy between a selected perception lane edge candidate and a best fit map edge candidate for a lane characteristic based on a posterior association probability matrix and a discrepancy flag. The one or more controllers segment the pair of inconsistent perception and map lane edges into one or more matching segments. The one or more matching segments indicate where the perception lane edge and the map lane edge match one another based on the lane characteristics. The perception and map edge association system improves localization and scene creation performance.

Abstract: A perception and map edge association system for an autonomous vehicle includes one or more controllers executing instructions to receive a plurality of perception lane edge candidates that are each representative of a perception lane edge and a plurality of map lane edge candidates that are each representative of a map lane edge. The one or more controllers identify a discrepancy between a selected perception lane edge candidate and a best fit map edge candidate for a lane characteristic based on a posterior association probability matrix and a discrepancy flag. The one or more controllers segment the pair of inconsistent perception and map lane edges into one or more matching segments. The one or more matching segments indicate where the perception lane edge and the map lane edge match one another based on the lane characteristics. The perception and map edge association system improves localization and scene creation performance.